My solutions to Andrew Ng's Machine Learning on Coursera, implemented in python.
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Fixed up some bugs in the code, improved a function that adds bias term, and improved the markdown documentation/explanations
12ac50aView on GitHubAdded new explanations for the regression model, and cleared some TODOs
8a57421View on GitHubrefactored some code, and tidied up descriptive explanations
54b4aa4View on GitHubWIP Exercise 7: Completed all K-means and a bit of PCA
93f8341View on GitHubExercise 6 WIP: completed SVM exercises, left with spam classification
8632ef0View on GitHubAdded new reference on the topic of numerical optimization, and updates some work documentation in a markdown cell
b035624View on GitHubWIP: Completed ex5, with largely correct results, but still some discrepancies to be fixed
bb27625View on GitHubFixed a bug in computation of points for the visualization of regression, that previously resulted in a wrong plane being drawn
c104e69View on GitHubEliminated bug in neural network forward pass function that resulted in use of a global variable rather than a local variable for computation
4ad6c9cView on GitHubCompleted programming exercise 4, but expect future improvements to the code
3715c9cView on GitHubRemoved a redundant np.sum() call within the logistic regression cost function
46d9cf5View on GitHubUpdated exercise 3 with explanation for one-vs-all classifier discrepancy based on different optimization algos
6489f7aView on GitHub